In this paper, we discuss the application of Natural Language Processing (NLP) techniques to improving speech prostheses for people with severe motor disabilities. Many people who are unable to speak because of physical disability utilize text-to-speech generators as prosthetic devices. However, users of speech prostheses very often have more general loss of motor control and, despite aids such as word prediction, inputting the text is slow and difficult. For typical users, current speech prostheses have output rates which are less than a tenth of the speed of normal speech. We are exploring various techniques which could improve rates, without sacrificing exibility of content. Here we describe the statistical word prediction techniques used in a communicator developed at CSLI and some experiments on improving prediction performance. We discuss the limitations of prediction on free text, and outline work which is in progress on utilizing constrained NL generation to make more natural interactions possible.

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